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> the published result is almost surely not repeatable in each nuanced case for day to day practitioners that have custom layers, extra regularization, heteroge
by jph00 8y ago
> the published result is almost surely not repeatable in each nuanced case for day to day practitioners that have custom layers, extra regularization, heterogeneous hardware for training
As shown in the post and linked code, it works very well with custom layers and extra regularization. Hardware does not impact the results at all. The results hold across CNNs, RNNs, and even QRNNs, across a wide variety of datasets.
We literally tried to find a real-world NLP or vision dataset it didn't work with, and failed to do so.
- mlthoughts2018 8y agohttps://news.ycombinator.com/item?id=17453802 https://news.ycombinator.com/item?id=17453802
- master_yoda_1 8y agoThis is working against the fundamental theorem of machine learning "there is no free lunch". (i.e. there is no "one for all" solution in ml) There is no explanation about why it worked. With that understanding it is very easy to generate a synthetic counter example where it does not work.